Algorithmic planners occasionally waste effort and thus computing time trying to solve certain tasks, as they often lack the human ability to recognize essential paths. These essential paths, termed landmarks, are vital for optimizing planning processes. This study revisits landmark-based planning methods introduced by Richter, Helmert, and Westphal in their 2008 paper, adapting and implementing them within a different framework, SymbolicPlanners, using the Julia programming language. The primary research question explores the performance of using landmarks as intermediary goals and pseudo-heuristics in the SymbolicPlanner framework. Sub-questions delve into the effectiveness of specific planning strategies, such as A^∗ Planner with GoalCount and HAdd heuristics, as well as planners utilizing landmarks. Evaluation over diverse domains reveals that LM^Local  and LM^LocalSmart outperform the basic GoalCount
heuristic and are on par with the HAdd heuristic. LM^Count, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LM^LocalSmart and limiting factors in the SymbolicPlanner framework.