In the literature on optimal stopping, the problem of maximizing the expected discounted reward over all stopping times has been explicitly solved for some special reward functions (including $(x^+)^ν$, $(e^x-K)^+$, $(K-e^{-x})^+$, $x\in\mathbb{R}$, $ν\in(0,\infty)$ and $K>0$) under general random walks in discrete time and Lévy processes in continuous time (subject to mild integrability conditions). All of such reward functions are continuous, increasing and logconcave while the corresponding optimal stopping times are of threshold type (i.e. the solutions are one-sided). In this paper, we show that all optimal stopping problems with increasing, logconcave and right-continuous reward functions admit one-sided solutions for general random walks and Lévy processes. We also investigate in detail the principle of smooth fit for Lévy processes when the reward function is increasing and logconcave.