Heuristic searches such as A* search are a popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality, simplicity in implementation and consistent behavior. In planning for robotic manipulation, however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. In this paper, we present a heuristic search-based approach to motion planning for manipulation that does deal effectively with the high-dimensionality of the problem. The paper presents a summary of the approach along with applications to single-arm and dual-arm motion planning with upright constraints on a PR2 robot operating in non-trivial cluttered spaces. An extensive experimental analysis in both simulation and on a physical PR2 shows that, in terms of runtime, our approach is on par with other most common sampling-based approaches and due to its deterministic cost-minimization, the computed motions are of good quality and are consistent, i.e. the resulting plans tend to be similar for similar tasks.