This thesis presents research involving sampling-based planning to study hybrid systems. This work has provided experimental success in understanding hybrid systems and their reachability properties. We approach this problem from a samplingbased planning perspective by applying the rapidly-exploring random tree (RRT) algorithm previously used in motion-planning problems. To this end, we have extended a current graphical tool for sampling-based planning, the Motion Strategy Library (MSL), to accept problems that are hybrid in nature. In addition, this thesis also furthers the study of the RRT algorithm by presenting new visual and statistical results.