Convolution of continuous and discrete distributions
$\newcommand{\+}{^{\dagger}}% \newcommand{\angles}[1]{\left\langle #1 \right\rangle}% \newcommand{\braces}[1]{\left\lbrace #1 \right\rbrace}% \newcommand{\bracks}[1]{\left\lbrack #1 \right\rbrack}% \newcommand{\ceil}[1]{\,\left\lceil #1 \right\rceil\,}% \newcommand{\dd}{{\rm d}}% \newcommand{\ds}[1]{\displaystyle{#1}}% \newcommand{\equalby}[1]{{#1 \atop {= \atop \vphantom{\huge A}}}}% \newcommand{\expo}[1]{\,{\rm e}^{#1}\,}% \newcommand{\fermi}{\,{\rm f}}% \newcommand{\floor}[1]{\,\left\lfloor #1 \right\rfloor\,}% \newcommand{\half}{{1 \over 2}}% \newcommand{\ic}{{\rm i}}% \newcommand{\iff}{\Longleftrightarrow} \newcommand{\imp}{\Longrightarrow}% \newcommand{\isdiv}{\,\left.\right\vert\,}% \newcommand{\ket}[1]{\left\vert #1\right\rangle}% \newcommand{\ol}[1]{\overline{#1}}% \newcommand{\pars}[1]{\left( #1 \right)}% \newcommand{\partiald}[3][]{\frac{\partial^{#1} #2}{\partial #3^{#1}}} \newcommand{\pp}{{\cal P}}% \newcommand{\root}[2][]{\,\sqrt[#1]{\,#2\,}\,}% \newcommand{\sech}{\,{\rm sech}}% \newcommand{\sgn}{\,{\rm sgn}}% \newcommand{\totald}[3][]{\frac{{\rm d}^{#1} #2}{{\rm d} #3^{#1}}} \newcommand{\ul}[1]{\underline{#1}}% \newcommand{\verts}[1]{\left\vert\, #1 \,\right\vert}$ $\ds{{\rm F}\pars{z} = \int_{-\infty}^{\infty}{\rm P}\pars{x}{\rm G}\pars{z-x} \,\dd x}$
If ${\rm P}\pars{x}$ is discrete, we can write it as $\ds{{\rm P}\pars{x} = \sum_{n}P_{n}\,\delta\pars{x - x_{n}}}$ where $\ds{\braces{P_{n}}}$ is the probability of $x_{n}$. Then, $$\color{#0000ff}{\large% {\rm F}\pars{z} = \int_{-\infty}^{\infty}{\rm P}\pars{x}{\rm G}\pars{z-x}\,\dd x = \sum_{n}P_{n}\,{\rm G}\pars{z - x_{n}}} $$
If the random variables are independent, you can use the characteristic functions of the Random variables since if:
$$Sn=\sum_{i=1}^{n}a_iX_i$$
then
$$\phi_{S_n}(t)=\prod_{i=1}^n\phi_{X_i}(a_it)$$
So for the case given
$$\phi_{X}=1-\frac{1}{2}+\frac{1}{2}e^{it} \text{ and } \phi_Y=\frac{e^{it1}-e^{it0}}{it(1-0)}$$
so $$\phi_Z=\frac{e^{2it}-e^{it0}}{it(2-0)}$$ which is $U(0,2)$, not just close to.