Wheeled-legged robots have the ability to navigate in cluttered and irregular environments adapting the locomotion mode to the terrain perceived. To achieve this functionality, a locomotion planner is needed. In this work we present a hybrid search-based planner, which considers a set of modifiable motion primitives and a 2.5D traversability map acquired from the environment to generate navigation plans for the hybrid mobility robot CENTAURO. Our approach was validated in simulation and on the real wheeled-legged robot CENTAURO, demonstrating traversing capabilities in cluttered environments with various obstacles.