Many evolutionary and general-purpose search algorithms have been proposed for solving a broad range of single- and multi-objective combinatorial optimization problems. Despite their skillful design, it is still difficult to achieve a high-level fundamental understanding of why and when an algorithm is successful. From an engineering point of view, setting up a systematic and principled methodology to select or design an effective search algorithm is also a challenging issue which is attracting more and more attention from the research community. In this context, fitness landscape analysis is a well-established field for understanding the relation between the search space and algorithm(s) or their underlying components and parameters. In this tutorial, we expose and contrast the impact of fitness landscape geometries on the performance of search algorithms. A sound and concise summary of features characterizing the structure of a problem instance are identified. We also show the main methodologies to sample discrete search spaces and to predict the expected algorithm performance for unseen problems.