Podcasts have continued to grow in popularity over the last two decades, with more than 4.52 million podcasts and 584 million listeners across the globe in 2025. Developing effective search systems for web-scale podcast corpora is of vital importance. Previous research has approached the search task primarily through text representation using a single transcription of the audio content via automatic speech recognition (ASR) models. However, there is currently limited understanding about how variation in podcast representations influences ranking, retrieval, and relevance assessment. In this paper, we provide a comprehensive analysis of the effectiveness of the podcast search problem from three different sources of variation. First, we create a set of 5,745 unique, automatically generated query variations derived from the TREC 2020 and 2021 podcast track topic descriptions. Second, to enable a comprehensive and reproducible analysis, we build a diverse pool composed of a set of 17 different ranked retrieval runs (system variations) using state-of-the-art (SOTA) information retrieval (IR) systems for all of the query variations over a set of corpus (transcript) variations. Third, we use five large language models (LLMs) to automatically assess the relevance of query-document pairs resulting from every query-system-transcription combination, yielding over 1.8 million judged pairs. Our analysis studies the impact of these variations on both search and judgment effectiveness, resulting in a series of practical recommendations for podcast and audio retrieval tasks.